In outdoor environments global positioning systems (GPS) has become the standard in position tracking. The technology has come down in price, size and power consumption to the point that it is incorporated into nearly every mobile communications device. Given data and communication infrastructure and smart phone popularity, mobile applications for use in commerce transactions is a market that has intense interest in being developed. However, most consumer revenue transactions occur in a retail environment that are largely indoors where GPS signals are severely attenuated. This prevents the accurate and pinpoint location tracking of the consumer at the point of purchase and thus severely hinders the revenue sharing opportunities for the broad spectrum of interested parties that have a keen interest in it. Companies in the search engine, payment processing, cellular service provider, handset manufacturer spaces, as well as the retailers themselves are highly motivated to find a solution in order to capitalize on this new and significant source of additional revenue flow.
There are numerous proposed and implemented methods of enabling indoor navigation capability. One method uses radio signals of opportunity, such as WiFi access points that have been spatially pre-mapped within a specific building in order to triangulate the location of the receiving device. This is accomplished by means of measuring the signal strength of an identified WiFi signal and triangulation between two or more such signals to create the common point of intersection of the multiple circles as represented by their respective radii. This same method can also be accomplished by the deliberate placement of Bluetooth or Bluetooth Low Energy beacons for the sole purpose of locating. These aforementioned methods both suffer from reception strength or timing inaccuracies that directly translate into location uncertainty, multi-path errors arising from signal reflections off of radio reflective surfaces indoors, and problems of high water density obstructions (i.e. crowds of people) that absorb and attenuate the signals as well. Another problem with this approach is that it requires costly infrastructure that needs to be paid for and is also laborious when setting up as the indoor reception mapping has to be measured and collected by hand.
Another approach involves replacing the light bulbs and tubes in existing lighting fixtures with ones that can encode digital signals that are invisible to the human eye but that can be decoded by the cameras built into smart phones. This would allow the camera to decode the id of exact fixture under which the device to be tracked currently sits. Again, this suffers from the similar cost burdens of infrastructure build out, mapping labor and also that the system would not function if the phone or tablet remained in the user's pocket where the camera would have an obstructed view to the light fixture. It is worthy to note that most of the value of such indoor tracking systems is not to actually help the owner of the phone to navigate indoors, but in fact to help the companies who are interested in the consumer's location track their position indoors unbeknownst to them, thus enabling such features as push advertising based upon indoor geo-fenced retail boundaries, in other words it's about the money flow and not consumer convenience where the interest truly lies.
Another method relies upon simple dead reckoning using compass and pedometer, but this is also flawed in that the phone has to be held in a known orientation so that the direction of travel can be measured by the phone's compass feature while its accelerometer is configured to count the frequency and approximate stride length during travel. This method suffers from the inaccuracies generated from the magnetic anomalies that greatly intensify indoors and also only works when the user is deliberately using their device to navigate in this manner. Again, this greatly reduces the interest in it by the companies who wish to profit from the user's indoor location.
And finally, there exists an approach by which the entire indoor magnetic field of a building is laboriously mapped a priori (Indoor Atlas) and via a combination of magnetic field map matching and the techniques of dead reckoning described previously, the location of the phone is tracked using the unique spatially located magnetic signatures of the building and less stringent distance of travel information provided by the dead reckoning. This method also suffers from the requirement of laborious a priori mapping of every possible route of travel located within a building, the mapping of the objects and obstructions of the building, the requirement that the device also be used in a fixed position relative to the consumer (i.e. not in the pocket), and also that magnetic fields are not necessarily always singularly unique in each unique part of the building and can change significantly over time (charging and discharging due to natural aging and domain relaxation) via magnetizing events such as a passing subway train that can highly charge ferrous objects in its vicinity based upon the large amounts of electrical currents that flow through its motors and tracks, and finally that objects such as furniture and inventory with their own magnetic signatures may be frequently moved around within the pre-mapped space thereby creating significant sources of error in the pre-mapped data. This last point may rely upon crowd-sourced input to reconfigure the magnetic mapping in close to real time based upon statistical methods as users traverse the mapped routes over the natural course of traffic flow, but this method is untested and can actually create more inaccuracies than it fixes. Except for the two last approaches, all the rest rely upon infrastructure development which is costly and not deployable quickly enough to create any critical mass to truly enable commerce. Also, there will be an ongoing issue of who owns the valuable indoor mapping data and who has access to it based upon who pays for and sets up the mapping infrastructure as well as who owns the real estate where it resides. Issues of intellectual property (the maps) are already being disputed, and this is creating one of the significant challenges to the broad based deployment of indoor navigation and the revenue opportunities resulting from it. Today's indoor navigation solutions currently employ a combination of several of these approaches discussed, but again, all are subject to their own aforementioned weaknesses, both technically and from a business perspective, and their combination only weakly remedies a few of their respective deficiencies.
A traditional navigation grade Inertial Navigation System (INS) would be the best technical solution for the problem of reliable and accurate indoor navigation, except for its size, cost and power consumption. Such an INS would be extremely accurate in tracking position and do so within an earth versus device reference frame, which means that such a device, if it could fit into a pocket, could accurately track its relative movements in three-dimensional space from an initial starting point (set by a last GPS fix immediately prior to entering a shopping center's entrance, for instance) for extended periods of time with great levels of accuracy. And because the tracking is based upon earth reference frame, the attitude of the device relative to its direction of travel does not factor into its ability to track position, so that position tracking from inside a purse or pants pocket would be possible. This is precisely how a consumer would want to use such a device, and is also how companies that want to commercialize indoor navigation would need this capability to work.
Unfortunately, the sensors that are found in today's smart phones do not possess anywhere near the performance necessary to enable true inertial navigation, especially the inertial sensors. The gyroscope, accelerometer and magnetometers that are deployed into today's cell phones optimize around size, cost and power consumption, which are parameters that are diametrically opposed to what is required for inertial navigation. The gyroscopes exhibit bias drift, noise and gain errors that are orders of magnitude greater than what is deployed in a typical INS, while the accelerometers do not have the range, resolution, accuracy, precision, signal to noise ratio and offset stability to do the job either. The magnetometer typically used in a cell phone is also severely degraded in capability with respect to high performance versions that are available on the market but not deployed in handsets. But as an INS often relies solely on its high performance inertial sensors, most do not contain a magnetometer.
In order to determine direction and distance traveled, the gyroscope in a typical INS tracks spatial orientation to within an error envelope of less than 0.1 degrees per hour over full range of dynamic motion, shock, vibration and temperature fluctuations. The INS accelerometers are then used to track distance traveled in any given direction through a double integration of its acceleration output. The problem with such an implementation on cell phone grade inertial sensors is that the gyros used often drift at a rate of a few degrees per minute and the accelerometers contain integration offset errors of a few feet per second. The requirement of a double integration of the accelerometers create two unknown constants of integration that do not remain stable with time or motion and thus the errors accumulated are so large that true displacement signals are completely overwhelmed by the noise introduced by such integration. What is therefore needed is a method to enable the use of non-inertial grade consumer sensors to provide indoor location tracking capabilities in a novel and useful manner.
It is desirable to have a method, system and apparatus for more accurately and inexpensively providing inertial navigation of a mobile device.